EEE 527 | Course Introduction and Application Information - M.SC. in Computer Engineering (Without Thesis)

Course Name

Principles of Autonomous Vehicle Design

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
EEE 527	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	Prof. Dr. Süha Bayındır
Course Lecturer(s)	Prof. Dr. Süha Bayındır
Assistant(s)	-

Course Objectives

This course aims at introducing the concepts of how autonomous cars operate and teaching the state of the art technologies required for localization, sensor fusion, SLAM, avoiding obstructions, recognizing the road lane markings, traffic signs, traffic prediction, lane level routing, reliability and security.

Learning Outcomes

The students who succeeded in this course;

Will be able to define components and principles of operation of autonomous vehicles
Will be able to design autonomous vehicle hardware and software
Will be able to apply localization and mapping methods using LIDAR, IMU, GPS and other sensors.
Will be able to develop simultaneous localization and mapping packages using ROS
Will be able to test the performance and reliability of autonomous vehicles under lab and industrial conditions

Course Description

In this course, localization, object recognition, tracking, sensor fusion, mapping, avoiding obstructions in autonomous vehicles will be explained and Python based perception, motion planning and navigation techniques using Robot operating System (ROS) environment will be taught.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction to Autonomus Driving, Sensing, Perception, Object Recognition & Tracking, ROS	Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap1
2	Sensing and Perceiving the Environment using wheel encoders, GPS, IMU, Ultrasonic Sensor & LIDAR	Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap2
3	Introduction to Robot Operating System (ROS)	Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap1
4	Introduction to Robot Operating System (ROS) Running ROS on Riders Cloud Platform	https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn
5	Creating And Configuring ROS Messages, publishers, subscribers and topics	https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn
6	ROS services, client – server applications	https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn
7	Kalman and Extended Kalman Filters, sensor fusion	https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn
8	Map based Navigation using ROS: Navigating a Autonomous Vehicle using Gazebo and RVIZ simulators in ROS	https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn
9	Tuning Navigation Stack Parameters, Pose of Vehicle And Transformation in 2D and 3D Reference Frames	https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn
10	Prediction & Routing, Traffic Prediction, Lane Level Routing	Shaoshan Liu et. al, “Creating Autonomous Vehicle Systems”, 2018, Chap5
11	Project work on TurtleBot3 using LIDAR, IMU, and Ultrasonic Sensor and Image Processing	TurtleBot3 Burger available in Mechatronics Lab
12	Project work on TurtleBot3 using LIDAR, IMU, and Ultrasonic Sensor and Image Processing	TurtleBot3 Burger available in Mechatronics Lab
13	Project work on TurtleBot3 using LIDAR, IMU, and Ultrasonic Sensor and Image Processing	TurtleBot3 Burger available in Mechatronics Lab
14	Project Presentations
15	Review of the Course
16	Final Exam

Course Notes/Textbooks	1. Creating Autonomous Vehicle Systems, Shaoshan Liu, Liyun Li, Jie Tang, Shuang Wu, Jean-Luc Gaudiot, Morgan & Claypool Publishers, 2017 2. Introduction to Driverless Self-Driving Cars, Lance B. EliotLBE Press Publishing, 2018.
Suggested Readings/Materials	1. Markus Maurer · J. Christian Gerdes Barbara Lenz · Hermann Winner, Autonomous Driving, Springer open, 2016 2. https://www.udemy.com/autonomous-cars-deep-learning-and-computer-vision-in-python/learn/ 3.http://emanual.robotis.com/docs/en/platform/turtlebot3/overview/

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project	1	45
Seminar / Workshop
Oral Exams
Midterm	1	25
Final Exam	1	30
Total

Weighting of Semester Activities on the Final Grade	2	70
Weighting of End-of-Semester Activities on the Final Grade	1	30
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16	5	80
Study Hours Out of Class			0
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project	1	50	50
Seminar / Workshop			0
Oral Exam			0
Midterms	1	22	22
Final Exam	1	25	25
		Total	225

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	-	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	-	-	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	-	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	-
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	-
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	-	-
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	-	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	-	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	-	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	-	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	-	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	-	-	-	-

EEE 527 | Course Introduction and Application Information

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

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ECTS / WORKLOAD TABLE

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